Data management method and device of weak current system, computer equipment and storage medium

Abstract

The invention discloses a data management method and device of a weak current system, computer equipment and a storage medium, and the method comprises the steps: collecting initial data through sensors disposed at a plurality of nodes of the weak current system, and transmitting the initial data to data collection equipment through a first communication protocol; performing first preprocessing on the initial data through the data acquisition equipment to obtain target data, and sending the target data to a data processing center through a second communication protocol; dividing the target data into a plurality of data fragments through the data processing center, and sending the plurality of data fragments to a plurality of first storage nodes in a distributed manner; and executing a corresponding copy strategy through each first storage node, and copying the data fragment to a second storage node, so that the second storage node stores the data fragment. Therefore, a huge amount of data generated by the weak current system can be safely and effectively managed.

Description

Technical Field

[0001] The present application belongs to the field of computer technology, and in particular, relates to a data management method and device for a weak current system, a computer device, and a storage medium. Background Art

[0002] In the current intelligent buildings, weak current system is an indispensable part, which includes multiple subsystems such as communication, monitoring, fire protection, security, etc. With the development of technology, the complexity of weak current system is increasing day by day, and the amount of data generated is also increasing sharply.

[0003] However, the applicant has found through research that the existing weak current system can often only perform simple data collection and monitoring, and lacks a safe and effective data management strategy.

[0004] Therefore, a new data management strategy for weak current systems is urgently needed to solve the above problems. Summary of the invention

[0005] In order to overcome the above-mentioned defects, the present application proposes a new data management strategy, which can safely and effectively manage the huge amount of data generated by the weak current system.

[0006] To solve the above problems, in a first aspect, the present application provides a data management method for a weak current system, the method comprising: collecting initial data through sensors arranged at multiple nodes of the weak current system, and transmitting the initial data to a data acquisition device through a first communication protocol; performing a first preprocessing on the initial data by the data acquisition device to obtain target data, and sending the target data to a data processing center through a second communication protocol; dividing the target data into multiple data fragments by the data processing center, and sending the multiple data fragments to multiple first storage nodes in a distributed manner; executing a corresponding copy strategy by each of the first storage nodes, and copying the data fragments to a second storage node, so that the second storage node stores the data fragments.

[0007] In an optional embodiment, the data processing center includes a buffer, and after sending the target data to the data processing center via a second communication protocol, the method further includes: storing the target data in the buffer, and performing a second preprocessing on the target data via the buffer.

[0008] In an optional embodiment, the first preprocessing includes at least one of the following: removing noise data; removing erroneous data; and standardizing the initial data.

[0009] In an optional embodiment, the initial data is standardized according to the following formula:

[0010]

[0011] Among them, x is the original data, μ is the mean of the data set, and σ is the standard deviation of the data set.

[0012] In an optional embodiment, after sending the target data to the data processing center via the second communication protocol, the method further includes: extracting energy consumption data from the target data; clustering the energy consumption data to determine energy consumption time periods; and classifying the energy consumption time periods via a support vector machine or a random forest algorithm to determine the type of each energy consumption time period.

[0013] In an optional embodiment, after sending the target data to the data processing center via the second communication protocol, the method further includes: performing fault detection on the target data via a time series analysis model; and issuing a fault prompt when it is detected that the target data meets the fault characteristics.

[0014] In an optional embodiment, after sending the target data to the data processing center via the second communication protocol, the method further includes: converting the format of the target data to obtain visualization data; processing the visualization data based on a visualization component, and displaying the processed visualization data on an interactive interface.

[0015] In a second aspect, the present application also provides a data management device for a weak current system, the device comprising: a sensor, which is arranged at multiple nodes of the weak current system, the sensor is used to collect initial data, and transmit the initial data to a data acquisition device through a first communication protocol; a data acquisition device, which is used to perform a first preprocessing on the initial data through the data acquisition device to obtain target data, and send the target data to a data processing center through a second communication protocol; a data processing center, which is used to divide the target data into multiple data fragments through the data processing center, and send the multiple data fragments to multiple first storage nodes in a distributed manner; a first storage node, which is used to execute a corresponding copy strategy through each of the first storage nodes to copy the data fragments to a second storage node; and a second storage node, which is used to store the data fragments.

[0016] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the program can implement the steps of any one of the above methods when executed by the processor.

[0017] In a fourth aspect, the present application also provides a storage medium for storing a computer program, which, when executed by a computer or a processor, implements the steps of any of the methods described above.

[0018] Compared with the prior art, the technical solution of the embodiment of the present application has the following beneficial effects:

[0019] The data management method of the weak current system according to the embodiment of the present application can safely and effectively manage the huge amount of data generated by the weak current system.

[0020] Specifically, the weak current system collects data through multiple internal sensors, and preprocesses the data collection equipment at the sensor end to reduce the computational pressure of subsequent data processing steps. Multiple data collection devices send the preprocessed data to the data processing center, which centrally processes the data and then partitions the processed data. Data partitioning can divide large-scale target data into small data fragments, which can be processed in a distributed manner to improve the reading and writing efficiency of data. Then, these data fragments are dispersedly stored on multiple first storage nodes, and the reliability and availability of other data can be guaranteed even in the event of failure of some storage nodes. Finally, the first storage node also stores copies of each data fragment in one or more second storage nodes according to the set copy mechanism, which can further enhance the stability and fault tolerance of the data, thereby realizing persistent storage of data.

[0021] Furthermore, the data processing center can perform energy consumption analysis on the weak current system based on the target data, identify peak and underestimated energy consumption periods in the weak current system, and thus help users identify and optimize energy consumption patterns.

[0022] Furthermore, the data processing center can identify faults in the weak current system based on the target data and detect possible faults in advance, thereby greatly improving the management efficiency and reliability of the weak current system.

[0023] Furthermore, an intuitive and interactive visualization module is designed. The core goal of this module is to convert the results of data analysis into an easy-to-understand graphical interface. The ability to convert complex data analysis results into intuitive graphics allows users to quickly grasp the operating status of the system and make effective management decisions without having to deeply understand the complex algorithms behind the data. The following is the final visualization, which not only improves the readability of the data, but also greatly improves management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of a flow chart of a data management method for a weak current system according to an embodiment of the present application;

[0025] Figure 2 A schematic diagram of the structure of a data management device for a weak current system according to an embodiment of the present application;

[0026] Figure 3A schematic diagram of a flow chart of an energy consumption analysis method according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] As mentioned in the background technology, the existing weak current system can usually only perform simple data collection and monitoring, and lacks a safe and effective data management strategy.

[0028] The applicant also found that the existing weak current system lacks in-depth analysis and utilization of big data. In addition, the existing weak current system mostly uses traditional charts in data display, which lacks intuitiveness and interactivity, making it difficult for managers to quickly and accurately understand the operating status of the system.

[0029] At present, the frontier direction of this field is mainly focused on how to use big data technology to effectively analyze the operating data of weak current systems, and how to improve the readability and management efficiency of data through visualization technology. However, the main challenges and problems include: real-time data collection and storage, accuracy of data analysis, scalability of the system and friendliness of the user interface. For example, the existing weak current system may not be able to process a large amount of data in real time, resulting in delayed analysis results and affecting the efficiency of management decisions.

[0030] In order to solve the above problems, the purpose of this application is to provide a visual data management platform for weak current systems, which can monitor the operating status of weak current systems in real time, and deeply explore the use of weak current equipment by integrating advanced data acquisition, storage, processing and analysis technologies. This technical solution aims to solve the problems of real-time data processing and energy consumption optimization existing in the prior art, and emphasizes its significance and value in the following ways: improving management efficiency and reducing operation and maintenance costs; achieving real-time monitoring and intelligent management of the operating status of weak current systems; improving data readability and interactivity through a visual interface; supporting remote data access and decision support, and providing a scientific basis for property management.

[0031] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the specific implementation methods and effects of the data management method, device, equipment, and storage medium of the weak current system proposed by the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0032] In order to solve the above problems, in the first aspect, the present application provides a data management method for a weak current system, which can be executed by the weak current system, and specifically by a controller arranged in the weak current system. In an optional embodiment, the weak current system can be arranged in an intelligent building to manage and control various devices in the intelligent building.

[0033] See also Figure 1 and Figure 2 , Figure 1This is a flow chart of a data management method for a weak current system of the present application. Figure 2 The schematic diagram of the structure of a data management device for a weak current system of the present application is shown in FIG. The data management device 20 for a weak current system (hereinafter referred to as the device 20) may include a sensor 201, a data acquisition device 202, a data processing center 203, a first storage node 204, and a second storage node 205. The following describes the steps S101 to S104 included in the data management method in conjunction with the structure of the device 20, and the details are as follows.

[0034] Step S101 , collecting initial data through sensors 201 arranged at multiple nodes of the weak current system, and transmitting the initial data to the data collection device 202 through a first communication protocol.

[0035] Among them, the sensor 201 may include at least one of the following: a temperature sensor, a humidity sensor, a light sensor, a personnel detection sensor, a voltage sensor, a current sensor, a power sensor, etc. Multiple sensors 201 are carefully deployed at each key node of the weak current system to ensure that the sensor can comprehensively and uninterruptedly collect the status information in the weak current system. The initial data is the unprocessed data collected by each sensor 201, such as the voltage signal collected by the voltage sensor, the current signal collected by the current sensor, etc.

[0036] The data acquisition device 202 is a data processing and forwarding module directly connected to each sensor 201 .

[0037] The first communication protocol is a communication protocol between the sensor 201 and the data acquisition device 202. The sensor 201 and the data acquisition device 202 may use wired communication or wireless communication, and correspondingly, the first communication protocol may be a wired communication protocol or a wireless communication protocol.

[0038] Among them, the communication interface corresponding to the first communication protocol may include RS-485, Modbus, etc.

[0039] Preferably, the sensor 201 and the data acquisition device 202 can communicate via wired communication to ensure the stability and timeliness of data transmission between the two and avoid being affected by the signal strength of the wireless network.

[0040] Step S102 , performing a first preprocessing on the initial data by the data acquisition device 202 to obtain target data, and sending the target data to the data processing center 203 through a second communication protocol.

[0041] In an optional embodiment, the sensor 201 and the data acquisition device 202 convert the acquired parameters, such as current, voltage, power, etc., into digital signals. Furthermore, the conversion of digital signals can be achieved through an analog-to-digital converter (ADC).

[0042] In an optional embodiment, the first preprocessing includes at least one of the following: removing noise data; removing erroneous data; and standardizing the initial data.

[0043] The data acquisition device 202 performs a first preprocessing on the initial data (including preliminary cleaning and formatting) to ensure the quality and consistency of the data, thereby removing noise and erroneous data in the initial data and reducing the amount of data for subsequent processing. The first preprocessing unifies the initial data into the same data format, which can also reduce the computational pressure of subsequent data processing steps and ensure the real-time and accuracy of the collected data, laying a solid foundation for subsequent data analysis and visualization.

[0044] Optionally, the initial data may be standardized according to the following formula:

[0045]

[0046] Among them, x is the original data, μ is the mean of the data set, and σ is the standard deviation of the data set.

[0047] The following is a specific example of data normalization:

[0048] Assume that a current sensor detects a current value of I, which is converted into a digital signal by ADC. The digital signal is transmitted to the data acquisition device via the Modbus protocol. The data acquisition device preprocesses I, calculates its deviation from the average current value μ_I and the standard deviation σ_I, and obtains the standardized current value I normalized Finally, I normalized The data is encrypted together with other data after the first preprocessing and transmitted to the data processing center 203 through the network.

[0049] In an optional embodiment, each sensor 201 is equipped with a data acquisition device 202, and the data acquisition device 202 can pre-process the initial data collected by the sensor 201 in real time, thereby effectively improving the efficiency of the pre-processing.

[0050] In another optional embodiment, multiple sensors 201 in each area are equipped with a data acquisition device 202, and the data acquisition device 202 is responsible for preprocessing the initial data collected by the multiple sensors 201 in the area. This can reduce the layout cost of the data acquisition device. In addition, multiple sensors 201 and data acquisition devices 202 in the same area can communicate based on the intranet of the area (which can be a wired network or a wireless network) to ensure the stability of communication.

[0051] The second communication protocol is generally a wireless communication protocol. The data collected by all sensors 201 in the weak current system are transmitted to the data processing center 203. The data processing center 203 has data integration technology and can identify and convert different data formats so that all data can be uniformly processed and analyzed.

[0052] Optionally, to ensure the real-time and security of data transmission, the following transmission protocols are used:

[0053]

[0054] Among them, Data is the data to be transmitted, Encryption means encrypting the data to be transmitted, EncryptedData is the encrypted data, Transmission means transmitting the encrypted data, and DataCenter is the data processing center.

[0055] In an optional embodiment, the data processing center 203 includes a buffer zone, Figure 1 After step S102, the data management method for the weak current system may further include: storing the target data in a buffer, and performing a second preprocessing on the target data through the buffer.

[0056] Specifically, after the data processing center 203 obtains the target data sent by the multiple data acquisition devices 202, it first sends the data to the data buffer, which is responsible for temporarily storing the data and performing the second preprocessing. The target data after the second preprocessing is allocated to different data partitions and transmitted to each first storage node 204 through the network.

[0057] The second preprocessing includes but is not limited to: unifying the format of the target data; fusing and summarizing the target data to form a unified data set.

[0058] Step S103 : dividing the target data into a plurality of data segments through the data processing center 203 , and sending the plurality of data segments to a plurality of first storage nodes 204 in a distributed manner.

[0059] Optionally, after the data processing center 203 integrates and summarizes the acquired target data, it can perform data partitioning, that is, the data processing center 203 can divide the large-scale target data into multiple small blocks (that is, data fragments), and these data fragments are distributedly stored on multiple first storage nodes 204 to improve data reading and writing efficiency.

[0060] Step S104: The first storage nodes 204 execute the corresponding copy strategy to copy the data fragments to the second storage nodes 205, so that the second storage nodes 205 store the data fragments.

[0061] Specifically, after each first storage node 204 receives the data fragments sent by the data processing center 203, it executes the corresponding replication mechanism, that is, each first storage node 204 creates one or more replicas for each data fragment, and stores these replicas in a distributed manner on one or more second storage nodes 205 to enhance the fault tolerance of the data, thereby completing the persistent storage of the data.

[0062] pass Figure 1 The method is that the weak current system collects data through multiple internal sensors 201, and the data collection device 202 at the sensor 201 end performs preprocessing to reduce the calculation pressure of the subsequent data processing steps. Multiple data collection devices 202 send the preprocessed data to the data processing center 203, which centrally processes these data and then partitions the processed data. Data partitioning can divide large-scale target data into small data fragments, and these data fragments can be distributedly processed to improve the reading and writing efficiency of data. Then, these data fragments are dispersedly stored on multiple first storage nodes 204, and even if some storage nodes fail, the reliability and availability of other data can be guaranteed. Finally, the first storage node 204 also stores the copies of each data fragment in one or more second storage nodes 205 according to the set copy mechanism, which can further enhance the stability and fault tolerance of the data, thereby realizing the persistent storage of data.

[0063] The following provides a Figure 1 Specific examples of data management methods in:

[0064] Sensor A (201) collects current data and sends it to a data acquisition device 202 via a first communication protocol; after receiving the data, the data acquisition device 202 performs a first preprocessing on the data according to a predetermined formatting rule and then sends it to a data processing center 203; an integrated processor in the data processing center 203 integrates the processed current data with data collected by other sensors (such as voltage data) to form a complete data record.

[0065] Assuming that the data stream collected by sensor A is generated at a rate of 100 records per second, the data buffer in the data processing center 203 will temporarily store these records and perform format verification; the data buffer will divide the verified data according to the timestamp, for example, every 10 minutes of data is a data segment; the divided multiple data segments are transmitted to the first storage node 204 of the distributed file system, and each first storage node 204 is responsible for storing a part of the data; after receiving the data, each first storage node 204 immediately executes the copy strategy to synchronize the data to at least two second storage nodes 205.

[0066] In an optional embodiment, after the data processing center 203 obtains the target data from each data acquisition device 202, it can analyze and apply these target data. In this link, advanced big data analysis algorithms can be used to deeply mine and analyze the target data.

[0067] It should be noted that the data processing center 203 can directly analyze and apply the target data after receiving the target data from the data acquisition device 202. Alternatively, the data processing center 203 can also call data from the first storage node 204 and / or the second storage node 205, and analyze and apply the called data.

[0068] Three application examples of target data are provided below.

[0069] In the first application example, the energy consumption analysis of the weak current system can be performed based on the target data. Figure 1 and Figure 3 , Figure 3 A flow chart of an energy consumption analysis method. Figure 1 After step S102, the method may further include:

[0070] Step S301, extracting energy consumption data from the target data;

[0071] Step S302, clustering the energy consumption data to determine energy consumption time periods;

[0072] Step S303: classify the energy consumption time periods by using a support vector machine (SVM) or a random forest algorithm to determine the type of each energy consumption time period.

[0073] Among them, the energy consumption data is data used to reflect the energy consumption of the weak current system, such as the power, usage time, voltage, current, etc. of the electrical equipment.

[0074] The type of energy consumption period can be set based on demand, such as including peak periods and low-consumption periods of energy consumption.

[0075] Optionally, the energy consumption data may be clustered using a K-means algorithm.

[0076] Furthermore, the energy consumption data can be clustered using the data model reflected by the following formula:

[0077]

[0078] Where J is the objective function, k is the number of clusters, S i is the set of data points in the ith cluster, μ i is the center point of the i-th cluster, ||x―μ i || represents vector x and vector μ i The measurement between.

[0079] The following is a specific example of energy consumption analysis for weak current systems:

[0080] Assume that we have collected energy consumption data for a period of time. We first use the K-means algorithm to cluster these energy consumption data and find the high energy consumption patterns. By analyzing the clustering results, we can determine several peak energy consumption periods and use the SVM algorithm to classify these periods for subsequent energy efficiency optimization.

[0081] pass Figure 3 According to the provided strategy, the data processing center 203 can perform energy consumption analysis on the weak current system based on the target data, identify the peak energy consumption period and the underestimated energy consumption period in the weak current system, thereby helping users to identify and optimize energy consumption patterns.

[0082] In the second application example, fault diagnosis of the weak current system can be performed based on the target data. Figure 1 , Figure 1 After step S102, the method may further include: performing fault detection on the target data through a time series analysis model; and issuing a fault prompt when it is detected that the target data meets the fault characteristics.

[0083] Optionally, the following time series analysis models can be used to monitor real-time data to identify potential failure modes:

[0084] X t =α+βX t―1 +∈ t

[0085] Among them, X t is the observed value at time t, α and β are model parameters, ∈ t is the error term.

[0086] Optionally, a fault feature library is established based on historical fault data, which contains typical features of various faults. The target data acquired in real time is monitored through a time series analysis model. Once it is detected that the data pattern of the target data is similar to the data pattern in the fault feature library, the system will issue a fault pre-diagnosis warning.

[0087] Through the solution of this embodiment, possible faults in the weak current system can be discovered in advance, thereby greatly improving the management efficiency and reliability of the weak current system.

[0088] In a third application embodiment, a data display method may also be provided. Figure 2 The device 20 may further include an interactive interface 206. For details, see Figure 3 , Figure 1 After step S102 , the method may further include: converting the target data into a format to obtain visualization data; processing the visualization data based on a visualization component, and displaying the processed visualization data on the interactive interface 206 .

[0089] Among them, the original data is converted into a format that can be recognized by the visualization component through data mapping, thereby realizing format conversion.

[0090] In an optional embodiment, after the data processing center 203 receives the target data from the data acquisition device 202, the target data may be directly converted into a format for display.

[0091] In another optional embodiment, the data processing center 203 may also call data from the first storage node 204 and / or the second storage node 205 and perform format conversion for display.

[0092] Visualization components can include various forms, such as line charts, bar charts, pie charts, and heat maps, to meet the display needs of different types of data.

[0093] In addition, an interactive interface 206 can be developed to allow the user to view data in at least one of the following ways: zooming and scrolling to view data of different time ranges; clicking on a specific data point to obtain more detailed information; applying filters to view data under specific conditions.

[0094] After the target data undergoes the data conversion step, it is sent to the visualization component for processing. For example, if the data is an energy consumption trend, a line chart component may be used to display it. The visualization component can also dynamically generate charts based on the characteristics of the data. For example, a bar chart may show a comparison of energy consumption of different devices. The interactive interface 206 allows the user to perform various operations, such as selecting a specific date range to view energy consumption, or viewing detailed information about the device by clicking on a bar in the bar chart.

[0095] The following is a specific example of data presentation:

[0096] When users open the visualization interface, the first thing they see is the overall energy consumption trend line chart. Users can use the controls on the interface to choose to view energy consumption data for "today", "this week" or "this month". When users are interested in the increase in energy consumption in a specific time period, they can click on the line segment, and the interface will pop up detailed information, including analysis of the reasons for the increase in energy consumption.

[0097] Through the data display method of this embodiment, an intuitive and interactive visualization display module is designed. The core goal of this module is to convert the results of data analysis into an easy-to-understand graphical interface. It can convert complex data analysis results into intuitive graphics, so that users can quickly grasp the operating status of the system and make effective management decisions without having to deeply understand the complex algorithms behind the data. The following is the final visualization display, which not only improves the readability of the data, but also greatly improves management efficiency.

[0098] See attached Figure 2 , Figure 2 FIG. 2 is a schematic diagram of the structure of a data management device 20 for a weak current system according to an embodiment of the present application. Figure 2 As shown, the data management device 20 for the weak current system provided in this embodiment may mainly include:

[0099] Sensors 201 are arranged at multiple nodes of the weak current system, and are used to collect initial data and transmit the initial data to a data collection device through a first communication protocol;

[0100] The data acquisition device 202 is used to perform a first preprocessing on the initial data by the data acquisition device to obtain target data, and send the target data to the data processing center through a second communication protocol;

[0101] A data processing center 203, configured to divide the target data into a plurality of data segments through the data processing center, and send the plurality of data segments to a plurality of first storage nodes in a distributed manner;

[0102] The first storage node 204 is used to execute the corresponding copy strategy through each of the first storage nodes to copy the data fragment to the second storage node;

[0103] The second storage node 205 is used to store the data fragments.

[0104] Optionally, the first preprocessing includes at least one of the following: removing noise data; removing erroneous data; and standardizing the initial data.

[0105] Optionally, the initial data is standardized according to the following formula:

[0106]

[0107] Among them, x is the original data, μ is the mean of the data set, and σ is the standard deviation of the data set.

[0108] In an optional embodiment, the data processing center 203 includes a buffer, and the buffer is used to store the target data and perform a second preprocessing on the target data after the data processing center 203 receives the target data.

[0109] In an optional embodiment, after the data processing center 203 receives the target data, the data management device 20 of the weak current system also includes an energy consumption analysis module, which is used to implement the following steps: extracting energy consumption data from the target data; clustering the energy consumption data to determine the energy consumption time period; classifying the energy consumption time period by a support vector machine or a random forest algorithm to determine the type of each energy consumption time period.

[0110] In an optional embodiment, after the data processing center 203 receives the target data, the data management device 20 of the weak current system also includes a fault analysis module, which is used to implement the following steps: performing fault detection on the target data through a time series analysis model; when it is detected that the target data meets the fault characteristics, issuing a fault prompt.

[0111] In an optional embodiment, after the data processing center 203 receives the target data, the data management device 20 of the weak current system also includes a data display module, which is used to implement the following steps: converting the format of the target data to obtain visual data; processing the visual data based on a visual component, and displaying the processed visual data on an interactive interface.

[0112] For more details about the working principle and working mode of the data management device 20 for the weak current system, please refer to the relevant description about the data management method for the weak current system, which will not be described in detail here.

[0113] The embodiment of the present application also provides a storage medium, which can be a computer-readable storage medium for storing a computer program, wherein the program is executed by a computer or a processor in any of the steps of the data management method for a weak current system. The computer-readable storage medium may include a non-volatile memory or a non-transitory memory, and may also include an optical disk, a mechanical hard disk, a solid-state hard disk, etc.

[0114] An embodiment of the present application also provides a computer device, which may include a computer device including a memory and a processor, wherein the memory stores a computer program, and when the program is executed by the processor, it can implement the steps of any one of the above-mentioned weak current system data management methods.

[0115] In the embodiments of the present application, the processor may be a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component, and may implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application may be directly embodied as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0116] In the embodiment of the present application, the memory may be a non-volatile memory, such as a hard disk drive (HDD) or a solid-state drive (SSD), etc., or a volatile memory (volatile memory), such as a random access memory (RAM). The memory is any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory in the embodiment of the present application may also be a circuit or any other device that can realize a storage function, for storing computer programs and / or data.

[0117] In the data management method of the weak current system provided in the embodiment of the present application, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program is loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user device or other programmable device. The computer program may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instruction may be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital video disc (DVD)), or a semiconductor medium (eg, an SSD).

[0118] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

[0119] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0120] In the above embodiments, the description of each embodiment has its own emphasis, and any multiple embodiments can be used in combination. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0121] In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in a processor or an instruction in the form of software. The steps of the method disclosed in conjunction with the embodiment of the present application can be directly embodied as a hardware processor for execution, or a combination of hardware and software units in a processor for execution. The software unit can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor executes the instructions in the memory, and completes the steps of the above method in conjunction with its hardware. To avoid repetition, it is not described in detail here.

[0122] In the embodiment of the present application, the processor of the above-mentioned device may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0123] The present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps of any method described in the above method embodiment. The computer program product may be a software installation package.

[0124] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the above-mentioned units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0125] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0126] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0127] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or TRP, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0128] It should be understood that the term "and / or" in this article is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article indicates that the associated objects before and after are in an "or" relationship.

[0129] The "plurality" appearing in the embodiments of the present application refers to two or more.

[0130] The first, second, etc. descriptions appearing in the embodiments of the present application are only used for illustration and distinction of the description objects. There is no order, nor do they indicate any special limitation on the number of devices in the embodiments of the present application, and cannot constitute any limitation on the embodiments of the present application.

[0131] The "connection" that appears in the embodiments of the present application refers to various connection methods such as direct connection or indirect connection to achieve communication between devices, and the embodiments of the present application do not impose any limitations on this.

[0132] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A data management method for a weak current system, characterized in that: The method comprises: Collecting initial data through sensors arranged at a plurality of nodes of the weak current system, and transmitting the initial data to a data acquisition device through a first communication protocol; Performing a first preprocessing on the initial data by the data acquisition device to obtain target data, and sending the target data to a data processing center through a second communication protocol; Splitting the target data into a plurality of data segments through the data processing center, and sending the plurality of data segments to a plurality of first storage nodes in a distributed manner; The data fragments are copied to the second storage nodes by executing the corresponding copy strategy through each of the first storage nodes, so that the second storage nodes store the data fragments.

2. The method according to claim 1, characterized in that The data processing center includes a buffer zone. After sending the target data to the data processing center through the second communication protocol, the method further includes: The target data is stored in a buffer, and a second preprocessing is performed on the target data through the buffer.

3. The method according to claim 1, characterized in that The first preprocessing includes at least one of the following: removing noise data; removing erroneous data; and standardizing the initial data.

4. The method according to claim 3, characterized in that The initial data is standardized according to the following formula: Among them, x is the original data, μ is the mean of the data set, and σ is the standard deviation of the data set.

5. The method according to any one of claims 1 to 4, characterized in that: After sending the target data to the data processing center via the second communication protocol, the method further includes: extracting energy consumption data from the target data; Clustering the energy consumption data to determine energy consumption time periods; The energy consumption periods are classified by using a support vector machine or a random forest algorithm to determine the type of each energy consumption period.

6. The method according to any one of claims 1 to 4, characterized in that: After sending the target data to the data processing center via the second communication protocol, the method further includes: Performing fault detection on the target data through a time series analysis model; When it is detected that the target data meets the fault characteristics, a fault prompt is issued.

7. The method according to any one of claims 1 to 4, characterized in that: After sending the target data to the data processing center via the second communication protocol, the method further includes: Convert the target data into a new format to obtain visual data; The visualization data is processed based on the visualization component, and the processed visualization data is displayed on the interactive interface.

8. A data management device for a weak current system, characterized in that: The device comprises: Sensors are arranged at multiple nodes of the weak current system, and the sensors are used to collect initial data and transmit the initial data to a data acquisition device through a first communication protocol; A data acquisition device, used for performing a first preprocessing on the initial data by the data acquisition device to obtain target data, and sending the target data to a data processing center through a second communication protocol; A data processing center, configured to divide the target data into a plurality of data segments through the data processing center, and send the plurality of data segments to a plurality of first storage nodes in a distributed manner; The first storage node is used to execute the corresponding copy strategy through each of the first storage nodes to copy the data fragment to the second storage node; The second storage node is used to store the data fragment.

9. A computer device comprising a processor and a storage device, wherein the storage device is suitable for storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and executed by the processor to perform the method of any one of claims 1 to 7.

10. A storage medium storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and executed by a processor to execute the method according to any one of claims 1 to 7.

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